A large-scale and high-resolution urban green space (UGS) product with 1 m of 31 major cities in China (UGS-1m) is generated based on a deep learning framework to provide basic UGS information for relevant UGS research, such as distribution, area, and UGS rate. Moreover, an urban green space dataset (UGSet) with a total of 4454 samples of 512 × 512 in size are also supplied as the benchmark to support model training and algorithm comparison.
Bingjie Li, Xiaocong Xu, Xiaoping Liu, Qian Shi, Haoming Zhuang, Yaotong Cai, and Da He
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-142,https://doi.org/10.5194/essd-2022-142, 2022
Preprint under review for ESSD
A global land cover map with fine spatial resolution (e.g., 30 m) is important for climate and environmental studies, food security, biodiversity conservation, carbon cycling, etc. In this study, we developed an improved global land cover map in 2015 with 30 m resolution (GLC-2015) by fusing multiple existing land cover products based on the Dempster-Shafer theory of evidence on the Google Earth Engine platform.
Terrestrial biosphere models that simulate both leaf dynamics and canopy photosynthesis are required to understand vegetation–climate interactions. A time-stepping scheme is proposed to simulate leaf area index, phenology, and gross primary production via climate variables. The method performs well on simulating deciduous broadleaf forests across the eastern United States; it provides a simplified and improved version of the growing production day model for use in land surface modeling.
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